It is common for a retailer to enlist the assistance of an advertising specialist to match various promotions to the retailer's customers. The advertising specialist may have demonstrated a methodology for smartly assigning such promotions, and the retailer may wish to employ that methodology in order to increase sales.
For this purpose, the retailer likely maintains a database of customer information. For example, in the context of a physical store which identifies walk-in customers using tokens (i.e., identifiers in place of actual credit card numbers and/or customer names), the retailer's database may store its customers spending habits such as what types of products each customer purchases, average purchase amount, purchasing frequency, zip code to determine customer demographics, and so on. As another example, in the context of an online store which identifies web shoppers via email addresses or the like, the retailer's database may store customer shipping addresses, shopping habits (i.e., listings of which items were viewed), browsing times, and so on.
When the time comes for the advertising specialist to match promotions to particular customers, the advertising specialist may read the customer information from the retailer's database, and apply special algorithms (e.g., filtering rules, analysis tools, matching routines, etc.) to select a particular advertisement for each customer. The advertising specialist then supplies these selections back to the retailer for distribution to the actual customers, e.g., the physical store may provide smartly-assigned discount coupons with sales receipts when its walk-in customers visit the check out counter, the online store may provide smartly-assigned banner ads on web pages and email promotions, and so on.